Title: An efficient customer classification framework to identify target customers by Bayesian networks in automotive industry

Authors: Amir Ebrahimzadeh Pilerood; Mohammad Reza Gholamian

Addresses: Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran ' Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran

Abstract: In this paper, an efficient customer classification framework is proposed which can be used instead of recency-frequency-monetary-based analysis as it preserves much more information about customers and at the same time it can consider different types of dependencies among purchases. Our proposed model is the best for industries with dependent and low number of purchases for each customer like automobile industry. In this model, by segmenting customers into different subsidiaries based on their number of purchases, an efficient customer classification model is developed to identify target customers. The proposed model is applied on one of the largest automobile manufacturer of the Middle-East and different performance metrics verified its efficiency.

Keywords: customer classification; target customers; Bayesian network classifiers; data mining; customer relationship management; CRM; automotive manufacturing; automobile industry; Bayesian networks; customer identification.

DOI: 10.1504/IJDATS.2016.081361

International Journal of Data Analysis Techniques and Strategies, 2016 Vol.8 No.4, pp.348 - 356

Available online: 05 Jan 2017 *

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